MIMO-OFDM wireless communications with MATLAB /

MIMO-OFDM wireless communications with MATLAB /

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Bibliographic Details
Imprint:Singapore ; Hoboken, NJ : IEEE Press : J. Wiley & Sons (Asia), c2010.
Description:1 online resource (xiv, 439 p.) : ill.
Language:English
Subject:
MATLAB.
MATLAB.
Orthogonal frequency division multiplexing.
MIMO systems.
MIMO systems.
Orthogonal frequency division multiplexing.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/8680493
Hidden Bibliographic Details
Other authors / contributors:Cho, Yong Soo.
ISBN:0470825634 (electronic bk.)
9780470825631 (electronic bk.)
9780470825624
0470825626
9780470825617 (cloth)
0470825618 (cloth)
Notes:Includes bibliographical references and index.
Description based on print version record.
Other form:Print version: MIMO-OFDM wireless communications with MATLAB. Singapore ; Hoboken, NJ : IEEE Press : J. Wiley & Sons (Asia), c2010 9780470825617
Standard no.:10.1002/9780470825631
Table of Contents:
  • Machine generated contents note: 1. The Wireless Channel: Propagation and Fading
  • 1.1. Large-Scale Fading
  • 1.1.1. General Path Loss Model
  • 1.1.2. Okumura/Hata Model
  • 1.1.3. IEEE 802.16d Model
  • 1.2. Small-Scale Fading
  • 1.2.1. Parameters for Small-Scale Fading
  • 1.2.2. Time-Dispersive vs. Frequency-Dispersive Fading
  • 1.2.3. Statistical Characterization and Generation of Fading Channel
  • 2. SISO Channel Models
  • 2.1. Indoor Channel Models
  • 2.1.1. General Indoor Channel Models
  • 2.1.2. IEEE 802.11 Channel Model
  • 2.1.3. Saleh-Valenzuela (S-V) Channel Model
  • 2.1.4. UWB Channel Model
  • 2.2. Outdoor Channel Models
  • 2.2.1. FWGN Model
  • 2.2.2. Jakes Model
  • 2.2.3. Ray-Based Channel Model
  • 2.2.4. Frequency-Selective Fading Channel Model
  • 2.2.5. SUI Channel Model
  • 3. MIMO Channel Models
  • 3.1. Statistical MIMO Model
  • 3.1.1. Spatial Correlation
  • 3.1.2. PAS Model
  • 3.2.I-METRA MIMO Channel Model
  • 3.2.1. Statistical Model of Correlated MIMO Fading Channel
  • 3.2.2. Generation of Correlated MIMO Channel Coefficients
  • 3.2.3.I-METRA MIMO Channel Model
  • 3.2.4.3GPP MIMO Channel Model
  • 3.3. SCM MIMO Channel Model
  • 3.3.1. SCM Link-Level Channel Parameters
  • 3.3.2. SCM Link-Level Channel Modeling
  • 3.3.3. Spatial Correlation of Ray-Based Channel Model
  • 4. Introduction to OFDM
  • 4.1. Single-Carrier vs. Multi-Carrier Transmission
  • 4.1.1. Single-Carrier Transmission
  • 4.1.2. Multi-Carrier Transmission
  • 4.1.3. Single-Carrier vs. Multi-Carrier Transmission
  • 4.2. Basic Principle of OFDM
  • 4.2.1. OFDM Modulation and Demodulation
  • 4.2.2. OFDM Guard Interval
  • 4.2.3. OFDM Guard Band
  • 4.2.4. BER of OFDM Scheme
  • 4.2.5. Water-Filling Algorithm for Frequency-Domain Link Adaptation
  • 4.3. Coded OFDM
  • 4.4. OFDMA: Multiple Access Extensions of OFDM
  • 4.4.1. Resource Allocation
  • Subchannel Allocation Types
  • 4.4.2. Resource Allocation
  • Subchannelization
  • 4.5. Duplexing
  • 5. Synchronization for OFDM
  • 5.1. Effect of STO
  • 5.2. Effect of CFO
  • 5.2.1. Effect of Integer Carrier Frequency Offset (IFO)
  • 5.2.2. Effect of Fractional Carrier Frequency Offset (FFO)
  • 5.3. Estimation Techniques for STO
  • 5.3.1. Time-Domain Estimation Techniques for STO
  • 5.3.2. Frequency-Domain Estimation Techniques for STO
  • 5.4. Estimation Techniques for CFO
  • 5.4.1. Time-Domain Estimation Techniques for CFO
  • 5.4.2. Frequency-Domain Estimation Techniques for CFO
  • 5.5. Effect of Sampling Clock Offset
  • 5.5.1. Effect of Phase Offset in Sampling Clocks
  • 5.5.2. Effect of Frequency Offset in Sampling Clocks
  • 5.6.Compensation for Sampling Clock Offset
  • 5.7. Synchronization in Cellular Systems
  • 5.7.1. Downlink Synchronization
  • 5.7.2. Uplink Synchronization
  • 6. Channel Estimation
  • 6.1. Pilot Structure
  • 6.1.1. Block Type
  • 6.1.2.Comb Type
  • 6.1.3. Lattice Type
  • 6.2. Training Symbol-Based Channel Estimation
  • 6.2.1. LS Channel Estimation
  • 6.2.2. MMSE Channel Estimation
  • 6.3. DFT-Based Channel Estimation
  • 6.4. Decision-Directed Channel Estimation
  • 6.5. Advanced Channel Estimation Techniques
  • 6.5.1. Channel Estimation Using a Superimposed Signal
  • 6.5.2. Channel Estimation in Fast Time-Varying Channels
  • 6.5.3. EM Algorithm-Based Channel Estimation
  • 6.5.4. Blind Channel Estimation
  • 7. PAPR Reduction
  • 7.1. Introduction to PAPR
  • 7.1.1. Definition of PAPR
  • 7.1.2. Distribution of OFDM Signal
  • 7.1.3. PAPR and Oversampling
  • 7.1.4. Clipping and SQNR
  • 7.2. PAPR Reduction Techniques
  • 7.2.1. Clipping and Filtering
  • 7.2.2. PAPR Reduction Code
  • 7.2.3. Selective Mapping
  • 7.2.4. Partial Transmit Sequence
  • 7.2.5. Tone Reservation
  • 7.2.6. Tone Injection
  • 7.2.7. DFT Spreading
  • 8. Inter-Cell Interference Mitigation Techniques
  • 8.1. Inter-Cell Interference Coordination Technique
  • 8.1.1. Fractional Frequency Reuse
  • 8.1.2. Soft Frequency Reuse
  • 8.1.3. Flexible Fractional Frequency Reuse
  • 8.1.4. Dynamic Channel Allocation
  • 8.2. Inter-Cell Interference Randomization Technique
  • 8.2.1. Cell-Specific Scrambling
  • 8.2.2. Cell-Specific Interleaving
  • 8.2.3. Frequency-Hopping OFDMA
  • 8.2.4. Random Subcarrier Allocation
  • 8.3. Inter-Cell Interference Cancellation Technique
  • 8.3.1. Interference Rejection Combining Technique
  • 8.3.2. IDMA Multiuser Detection
  • 9. MIMO: Channel Capacity
  • 9.1. Useful Matrix Theory
  • 9.2. Deterministic MIMO Channel Capacity
  • 9.2.1. Channel Capacity when CSI is Known to the Transmitter Side
  • 9.2.2. Channel Capacity when CSI is Not Available at the Transmitter Side
  • 9.2.3. Channel Capacity of SIMO and MISO Channels
  • 9.3. Channel Capacity of Random MIMO Channels
  • 10. Antenna Diversity and Space-Time Coding Techniques
  • 10.1. Antenna Diversity
  • 10.1.1. Receive Diversity
  • 10.1.2. Transmit Diversity
  • 10.2. Space-Time Coding (STC): Overview
  • 10.2.1. System Model
  • 10.2.2. Pairwise Error Probability
  • 10.2.3. Space-Time Code Design
  • 10.3. Space-Time Block Code (STBC)
  • 10.3.1. Alamouti Space-Time Code
  • 10.3.2. Generalization of Space-Time Block Coding
  • 10.3.3. Decoding for Space-Time Block Codes
  • 10.3.4. Space-Time Trellis Code
  • 11. Signal Detection for Spatially Multiplexed MIMO Systems
  • 11.1. Linear Signal Detection
  • 11.1.1. ZF Signal Detection
  • 11.1.2. MMSE Signal Detection
  • 11.2. OSIC Signal Detection
  • 11.3. ML Signal Detection
  • 11.4. Sphere Decoding Method
  • 11.5. QRM-MLD Method
  • 11.6. Lattice Reduction-Aided Detection
  • 11.6.1. Lenstra-Lenstra-Lovasz (LLL) Algorithm
  • 11.6.2. Application of Lattice Reduction
  • 11.7. Soft Decision for MIMO Systems
  • 11.7.1. Log-Likelihood-Ratio (LLR) for SISO Systems
  • 11.7.2. LLR for Linear Detector-Based MIMO System
  • 11.7.3. LLR for MIMO System with a Candidate Vector Set
  • 11.7.4. LLR for MIMO System Using a Limited Candidate Vector Set
  • Appendix 11 A Derivation of Equation (11.23)
  • 12. Exploiting Channel State Information at the Transmitter Side
  • 12.1. Channel Estimation on the Transmitter Side
  • 12.1.1. Using Channel Reciprocity
  • 12.1.2. CSI Feedback
  • 12.2. Precoded OSTBC
  • 12.3. Precoded Spatial-Multiplexing System
  • 12.4. Antenna Selection Techniques
  • 12.4.1. Optimum Antenna Selection Technique
  • 12.4.2.Complexity-Reduced Antenna Selection
  • 12.4.3. Antenna Selection for OSTBC
  • 13. Multi-User MIMO
  • 13.1. Mathematical Model for Multi-User MIMO System
  • 13.2. Channel Capacity of Multi-User MIMO System
  • 13.2.1. Capacity of MAC
  • 13.2.2. Capacity of BC
  • 13.3. Transmission Methods for Broadcast Channel
  • 13.3.1. Channel Inversion
  • 13.3.2. Block Diagonalization
  • 13.3.3. Dirty Paper Coding (DPC)
  • 13.3.4. Tomlinson-Harashima Precoding.

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